Inference for Bugs model at "/home/kubo/MattPasoh2010/winbugs/model.bug.txt", fit using WinBUGS, 3 chains, each with 10000 iterations (first 2000 discarded), n.thin = 20 n.sims = 1200 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff b[1] 0.019 0.003 0.014 0.017 0.019 0.021 0.025 1.008 330 b[2] -0.010 0.002 -0.014 -0.011 -0.010 -0.009 -0.006 1.005 460 b[3] 0.000 0.001 -0.002 -0.001 0.000 0.000 0.002 1.010 230 b.spc[1,1] -0.003 0.003 -0.009 -0.005 -0.003 0.000 0.004 1.006 340 b.spc[1,2] 0.026 0.004 0.019 0.024 0.026 0.029 0.034 1.003 650 b.spc[1,3] 0.005 0.003 -0.001 0.003 0.005 0.008 0.012 1.006 310 b.spc[1,4] -0.006 0.003 -0.012 -0.008 -0.005 -0.003 0.001 1.007 290 b.spc[1,5] -0.010 0.004 -0.018 -0.013 -0.010 -0.007 -0.002 1.002 940 b.spc[1,6] -0.001 0.003 -0.008 -0.003 -0.001 0.001 0.006 1.005 520 b.spc[1,7] -0.005 0.003 -0.013 -0.008 -0.005 -0.003 0.001 1.002 1100 b.spc[1,8] -0.003 0.003 -0.009 -0.005 -0.002 0.000 0.004 1.005 360 b.spc[1,9] -0.004 0.003 -0.010 -0.006 -0.004 -0.002 0.003 1.001 1200 b.spc[1,10] 0.017 0.004 0.010 0.014 0.017 0.019 0.024 1.009 210 b.spc[1,11] 0.005 0.004 -0.001 0.003 0.005 0.008 0.012 1.001 1200 b.spc[1,12] -0.006 0.003 -0.012 -0.008 -0.006 -0.003 0.001 1.004 530 b.spc[1,13] -0.004 0.003 -0.011 -0.007 -0.004 -0.002 0.002 1.005 400 b.spc[1,14] 0.003 0.003 -0.003 0.001 0.003 0.006 0.010 1.003 620 b.spc[1,15] -0.009 0.004 -0.017 -0.012 -0.009 -0.006 -0.001 1.000 1200 b.spc[1,16] -0.010 0.004 -0.019 -0.013 -0.010 -0.007 -0.002 1.000 1200 b.spc[2,1] -0.002 0.003 -0.008 -0.004 -0.002 0.001 0.005 1.002 1100 b.spc[2,2] -0.008 0.003 -0.013 -0.009 -0.007 -0.006 -0.002 1.006 500 b.spc[2,3] 0.001 0.003 -0.005 -0.001 0.001 0.003 0.007 1.000 1200 b.spc[2,4] 0.004 0.003 -0.002 0.002 0.004 0.006 0.009 1.001 1200 b.spc[2,5] -0.005 0.005 -0.014 -0.008 -0.005 -0.002 0.004 1.001 1200 b.spc[2,6] 0.002 0.003 -0.005 -0.001 0.002 0.004 0.008 1.008 400 b.spc[2,7] 0.003 0.003 -0.003 0.001 0.003 0.005 0.010 1.003 660 b.spc[2,8] 0.005 0.003 -0.001 0.003 0.005 0.007 0.011 1.001 1200 b.spc[2,9] -0.001 0.004 -0.008 -0.004 -0.001 0.001 0.006 1.001 1200 b.spc[2,10] -0.003 0.003 -0.009 -0.005 -0.003 0.000 0.003 1.005 480 b.spc[2,11] 0.004 0.003 -0.002 0.002 0.004 0.006 0.010 1.004 480 b.spc[2,12] -0.007 0.004 -0.016 -0.010 -0.007 -0.005 0.000 1.007 270 b.spc[2,13] -0.001 0.003 -0.008 -0.004 -0.001 0.001 0.006 1.004 420 b.spc[2,14] 0.001 0.003 -0.005 0.000 0.001 0.003 0.008 1.007 300 b.spc[2,15] 0.002 0.005 -0.007 -0.001 0.002 0.005 0.011 1.002 790 b.spc[2,16] 0.005 0.005 -0.004 0.002 0.005 0.008 0.014 1.002 1100 b.spc[3,1] 0.001 0.001 -0.001 0.000 0.001 0.001 0.003 1.010 240 b.spc[3,2] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.007 330 b.spc[3,3] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.008 290 b.spc[3,4] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.008 300 b.spc[3,5] 0.000 0.001 -0.002 -0.001 0.000 0.000 0.002 1.012 190 b.spc[3,6] 0.000 0.001 -0.002 0.000 0.000 0.001 0.002 1.009 250 b.spc[3,7] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.008 280 b.spc[3,8] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.008 330 b.spc[3,9] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.012 180 b.spc[3,10] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.011 200 b.spc[3,11] 0.000 0.001 -0.002 0.000 0.000 0.001 0.002 1.010 220 b.spc[3,12] 0.000 0.001 -0.002 -0.001 0.000 0.000 0.001 1.007 290 b.spc[3,13] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.009 270 b.spc[3,14] 0.000 0.001 -0.002 -0.001 0.000 0.001 0.002 1.008 290 b.spc[3,15] 0.001 0.001 -0.001 0.000 0.001 0.001 0.002 1.010 210 b.spc[3,16] 0.000 0.001 -0.001 0.000 0.000 0.001 0.002 1.008 290 tau.b[1] 8595.926 3352.134 3470.825 6099.000 8118.000 10600.000 16300.250 1.001 1200 tau.b[2] 26062.780 11731.653 9522.420 17217.499 24164.995 32704.990 54343.745 1.001 1200 tau.b[3] 73899.092 26780.755 31153.473 54139.900 69559.997 92507.499 130414.966 1.002 920 tau 2309.958 83.485 2150.950 2252.000 2310.000 2368.000 2474.100 1.000 1200 deviance -20391.283 79.240 -20550.000 -20440.000 -20390.000 -20340.000 -20240.000 1.000 1200 For each parameter, n.eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor (at convergence, Rhat=1). DIC info (using the rule, pD = Dbar-Dhat) pD = 3192.2 and DIC = -17198.9 DIC is an estimate of expected predictive error (lower deviance is better).